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      Recent technical advances in proteomics

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      F1000Research

      F1000 Research Limited

      proteomics, mass spectrometry, spatial proteomics, interaction proteomics

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          Abstract

          Mass spectrometry is one of the key technologies of proteomics, and over the last decade important technical advances in mass spectrometry have driven an increased capability for proteomic discovery. In addition, new methods to capture important biological information have been developed to take advantage of improving proteomic tools.

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          Most cited references 68

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          The BioPlex Network: A Systematic Exploration of the Human Interactome.

          Protein interactions form a network whose structure drives cellular function and whose organization informs biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7,668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80%-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. Network structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally related proteins. Finally, BioPlex, in combination with other approaches, can be used to reveal interactions of biological or clinical significance. For example, mutations in the membrane protein VAPB implicated in familial amyotrophic lateral sclerosis perturb a defined community of interactors.
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            Architecture of the human interactome defines protein communities and disease networks

            The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein-protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidation of how genome variation contributes to disease 1–3 . Here, we present BioPlex 2.0 (Biophysical Interactions of ORFEOME-derived complexes), which employs robust affinity purification-mass spectrometry (AP-MS) methodology 4 to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein coding genes from the human genome, and constitutes the largest such network to date. With >56,000 candidate interactions, BioPlex 2.0 contains >29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering (MCL) 5 of interacting proteins identified more than 1300 protein communities representing diverse cellular activities. Genes essential for cell fitness 6,7 are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.
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              A census of human soluble protein complexes.

              Cellular processes often depend on stable physical associations between proteins. Despite recent progress, knowledge of the composition of human protein complexes remains limited. To close this gap, we applied an integrative global proteomic profiling approach, based on chromatographic separation of cultured human cell extracts into more than one thousand biochemical fractions that were subsequently analyzed by quantitative tandem mass spectrometry, to systematically identify a network of 13,993 high-confidence physical interactions among 3,006 stably associated soluble human proteins. Most of the 622 putative protein complexes we report are linked to core biological processes and encompass both candidate disease genes and unannotated proteins to inform on mechanism. Strikingly, whereas larger multiprotein assemblies tend to be more extensively annotated and evolutionarily conserved, human protein complexes with five or fewer subunits are far more likely to be functionally unannotated or restricted to vertebrates, suggesting more recent functional innovations. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Journal
                F1000Res
                F1000Res
                F1000Research
                F1000Research
                F1000 Research Limited (London, UK )
                2046-1402
                29 March 2019
                2019
                : 8
                Affiliations
                [1 ]Molecular Medicine and Neurobiology, Scripps Research, 0550 North Torrey Pines Road, SR302, La Jolla, CA, 92037, USA
                Author notes

                No competing interests were disclosed.

                Article
                10.12688/f1000research.16987.1
                6441878
                Copyright: © 2019 Yates, III JR

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Funding
                Funded by: National Institute of Health
                Award ID: R01MH067880
                Award ID: P41GM133533
                This work was supported by the National Institutes of Health (grants R01 MH067880 and P41 GM133533).
                The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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